Args:

initial_clusters: specifies how to initialize the clusters for training.
See clustering_ops.kmeans for the possible values.

distance_metric: the distance metric used for clustering.
See clustering_ops.kmeans for the possible values.

random_seed: Python integer. Seed for PRNG used to initialize centers.

use_mini_batch: If true, use the mini-batch k-means algorithm. Else assume
full batch.

kmeans_plus_plus_num_retries: For each point that is sampled during
kmeans++ initialization, this parameter specifies the number of
additional points to draw from the current distribution before selecting
the best. If a negative value is specified, a heuristic is used to
sample O(log(num_to_sample)) additional points.

relative_tolerance: A relative tolerance of change in the loss between
iterations. Stops learning if the loss changes less than this amount.
Note that this may not work correctly if use_mini_batch=True.

config: See Estimator

clusters()

Returns cluster centers.

evaluate(*args, **kwargs)

See Evaluable. (deprecated arguments)

SOME ARGUMENTS ARE DEPRECATED. They will be removed after 2016-12-01.
Instructions for updating:
Estimator is decoupled from Scikit Learn interface by moving into
separate class SKCompat. Arguments x, y and batch_size are only
available in the SKCompat class, Estimator will only accept input_fn.
Example conversion:
est = Estimator(...) -> est = SKCompat(Estimator(...))

Raises:

ValueError: If at least one of x or y is provided, and at least one of
input_fn or feed_fn is provided.
Or if metrics is not None or dict.

export(*args, **kwargs)

Exports inference graph into given dir. (deprecated arguments)

SOME ARGUMENTS ARE DEPRECATED. They will be removed after 2016-09-23.
Instructions for updating:
The signature of the input_fn accepted by export is changing to be consistent with what's used by tf.Learn Estimator's train/evaluate. input_fn (and in most cases, input_feature_key) will become required args, and use_deprecated_input_fn will default to False and be removed altogether.

Args:

export_dir: A string containing a directory to write the exported graph
and checkpoints.

input_fn: If use_deprecated_input_fn is true, then a function that given
Tensor of Example strings, parses it into features that are then
passed to the model. Otherwise, a function that takes no argument and
returns a tuple of (features, labels), where features is a dict of
string key to Tensor and labels is a Tensor that's currently not
used (and so can be None).

input_feature_key: Only used if use_deprecated_input_fn is false. String
key into the features dict returned by input_fn that corresponds to a
the raw Example strings Tensor that the exported model will take as
input. Can only be None if you're using a custom signature_fn that
does not use the first arg (examples).

use_deprecated_input_fn: Determines the signature format of input_fn.

signature_fn: Function that returns a default signature and a named
signature map, given Tensor of Example strings, dict of Tensors
for features and Tensor or dict of Tensors for predictions.

prediction_key: The key for a tensor in the predictions dict (output
from the model_fn) to use as the predictions input to the
signature_fn. Optional. If None, predictions will pass to
signature_fn without filtering.

default_batch_size: Default batch size of the Example placeholder.

exports_to_keep: Number of exports to keep.

Returns:

The string path to the exported directory. NB: this functionality was
added ca. 2016/09/25; clients that depend on the return value may need
to handle the case where this function returns None because subclasses
are not returning a value.

export_savedmodel(*args, **kwargs)

Exports inference graph as a SavedModel into given dir. (experimental)

THIS FUNCTION IS EXPERIMENTAL. It may change or be removed at any time, and without warning.

Args:

export_dir_base: A string containing a directory to write the exported
graph and checkpoints.

input_fn: A function that takes no argument and
returns an InputFnOps.

default_output_alternative_key: the name of the head to serve when none is
specified.

assets_extra: A dict specifying how to populate the assets.extra directory
within the exported SavedModel. Each key should give the destination
path (including the filename) relative to the assets.extra directory.
The corresponding value gives the full path of the source file to be
copied. For example, the simple case of copying a single file without
renaming it is specified as
{'my_asset_file.txt': '/path/to/my_asset_file.txt'}.

as_text: whether to write the SavedModel proto in text format.

exports_to_keep: Number of exports to keep.

Returns:

The string path to the exported directory.

Raises:

ValueError: if an unrecognized export_type is requested.

fit(*args, **kwargs)

See Trainable. (deprecated arguments)

SOME ARGUMENTS ARE DEPRECATED. They will be removed after 2016-12-01.
Instructions for updating:
Estimator is decoupled from Scikit Learn interface by moving into
separate class SKCompat. Arguments x, y and batch_size are only
available in the SKCompat class, Estimator will only accept input_fn.
Example conversion:
est = Estimator(...) -> est = SKCompat(Estimator(...))

Raises:

ValueError: If x or y are not None while input_fn is not None.

ValueError: If both steps and max_steps are not None.

get_params(deep=True)

Get parameters for this estimator.

Args:

deep: boolean, optional

If True, will return the parameters for this estimator and
contained subobjects that are estimators.

Returns:

params : mapping of string to any
Parameter names mapped to their values.

get_variable_names()

Returns list of all variable names in this model.

Returns:

List of names.

get_variable_value(name)

Returns value of the variable given by name.

Args:

name: string, name of the tensor.

Returns:

Numpy array - value of the tensor.

partial_fit(*args, **kwargs)

Incremental fit on a batch of samples. (deprecated arguments)

SOME ARGUMENTS ARE DEPRECATED. They will be removed after 2016-12-01.
Instructions for updating:
Estimator is decoupled from Scikit Learn interface by moving into
separate class SKCompat. Arguments x, y and batch_size are only
available in the SKCompat class, Estimator will only accept input_fn.
Example conversion:
est = Estimator(...) -> est = SKCompat(Estimator(...))

This method is expected to be called several times consecutively
on different or the same chunks of the dataset. This either can
implement iterative training or out-of-core/online training.

This is especially useful when the whole dataset is too big to
fit in memory at the same time. Or when model is taking long time
to converge, and you want to split up training into subparts.

Args:

x: Matrix of shape [n_samples, n_features...]. Can be iterator that
returns arrays of features. The training input samples for fitting the
model. If set, input_fn must be None.

y: Vector or matrix [n_samples] or [n_samples, n_outputs]. Can be
iterator that returns array of labels. The training label values
(class labels in classification, real numbers in regression). If set,
input_fn must be None.

input_fn: Input function. If set, x, y, and batch_size must be
None.

steps: Number of steps for which to train model. If None, train forever.

batch_size: minibatch size to use on the input, defaults to first
dimension of x. Must be None if input_fn is provided.

monitors: List of BaseMonitor subclass instances. Used for callbacks
inside the training loop.

Returns:

self, for chaining.

Raises:

ValueError: If at least one of x and y is provided, and input_fn is
provided.

predict(*args, **kwargs)

Returns predictions for given features. (deprecated arguments)

SOME ARGUMENTS ARE DEPRECATED. They will be removed after 2016-12-01.
Instructions for updating:
Estimator is decoupled from Scikit Learn interface by moving into
separate class SKCompat. Arguments x, y and batch_size are only
available in the SKCompat class, Estimator will only accept input_fn.
Example conversion:
est = Estimator(...) -> est = SKCompat(Estimator(...))

Args:

x: Matrix of shape [n_samples, n_features...]. Can be iterator that
returns arrays of features. The training input samples for fitting the
model. If set, input_fn must be None.

outputs: list of str, name of the output to predict.
If None, returns all.

as_iterable: If True, return an iterable which keeps yielding predictions
for each example until inputs are exhausted. Note: The inputs must
terminate if you want the iterable to terminate (e.g. be sure to pass
num_epochs=1 if you are using something like read_batch_features).

Returns:

A numpy array of predicted classes or regression values if the
constructor's model_fn returns a Tensor for predictions or a dict
of numpy arrays if model_fn returns a dict. Returns an iterable of
predictions if as_iterable is True.

Raises:

ValueError: If x and input_fn are both provided or both None.

predict_cluster_idx(input_fn=None)

Yields predicted cluster indices.

score(input_fn=None, steps=None)

Predict total sum of distances to nearest clusters.

Note that this function is different from the corresponding one in sklearn
which returns the negative of the sum of distances.

Args:

input_fn: see predict.

steps: see predict.

Returns:

Total sum of distances to nearest clusters.

set_params(**params)

Set the parameters of this estimator.

The method works on simple estimators as well as on nested objects
(such as pipelines). The former have parameters of the form
<component>__<parameter> so that it's possible to update each
component of a nested object.

Args:

**params: Parameters.

Returns:

self

Raises:

ValueError: If params contain invalid names.

transform(input_fn=None, as_iterable=False)

Transforms each element to distances to cluster centers.

Note that this function is different from the corresponding one in sklearn.
For SQUARED_EUCLIDEAN distance metric, sklearn transform returns the
EUCLIDEAN distance, while this function returns the SQUARED_EUCLIDEAN
distance.

Args:

input_fn: see predict.

as_iterable: see predict

Returns:

Array with same number of rows as x, and num_clusters columns, containing
distances to the cluster centers.